def load(path): obj = Guesser() obj._cl = Classifier.load(path) return obj
q = sys.argv[1] cached = r.get(q) if not cached: url = 'http://search.twitter.com/search.json' req = requests.get(url, params={'q': sys.argv[1]}) data = json.loads(req.text) if 'results' not in data: print('Error') print(data) exit() cached = json.dumps(data['results']) r.setex(q, TTL, cached) results = json.loads(cached) cl = Classifier.load('test.svm') index = ir.SentimentIndex.load('test.index', 'delta', 'bogram') index.get_text = lambda x: x['text'] docs = [] for msg in results: feats = index.weight(index.features(msg)) docs.append(feats) labels = cl.predict(docs) for n in range(len(results)): print('{0}.\t{1}\t{2}'.format(n + 1, labels[n], results[n]['text']))